Gradient Descent is an algorithm for finding the minimum of a function.
def gradient(X_norm,y,theta,alpha,m,n,num_it): temp=np.array(np.zeros_like(theta,float)) for …
python numpy machine-learning linear-regression gradient-descentThe method zero_grad() needs to be called during training. But the documentation is not very helpful | zero_grad(self) | …
python neural-network deep-learning pytorch gradient-descentI'm trying to implement a neural network that classifies images into one of the two discrete categories. The problem is, …
python-3.x numpy neural-network deep-learning gradient-descentI am trying to build a neural network from scratch. Across all AI literature there is a consensus that weights …
machine-learning neural-network artificial-intelligence mathematical-optimization gradient-descentI want to set some of my model frozen. Following the official docs: with torch.no_grad(): linear = nn.Linear(1, 1) …
python pytorch gradient-descentWhat is the correct way to perform gradient clipping in pytorch? I have an exploding gradients problem, and I need …
python machine-learning deep-learning pytorch gradient-descentI've noticed that a frequent occurrence during training is NANs being introduced. Often times it seems to be introduced by …
machine-learning neural-network deep-learning caffe gradient-descentI understand what Gradient Descent does. Basically it tries to move towards the local optimal solution by slowly moving down …
machine-learning data-mining mathematical-optimization gradient-descent newtons-methodLooking at an example 'solver.prototxt', posted on BVLC/caffe git, there is a training meta parameter weight_decay: 0.04 What …
machine-learning neural-network deep-learning caffe gradient-descentI just try to find out how I can use Caffe. To do so, I just took a look at …
machine-learning neural-network deep-learning caffe gradient-descent